Interpretive Summary: Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) using natural populations or germplasm collections. A method was proposed to map simultaneously multiple QTLs by a regression method that also incorporates estimates of the population structure. In the present study, we extended this method to analyze ordinal and censored traits, since both types of trait are common in germplasm collection descriptors. Ordinal traits are measured on a continuous rather than categorical scale. Censored traits are continuous but have a maximum or minimum value beyond which they are not measured. In both models, we postulated the existence of an underlying continuous variable associated with the observable data. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses also showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant associations that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in germplasm collections.

Technical Abstract:
Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of trait are common in germplasm collection descriptors. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.